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Early prediction of the long-term evolution of multiple sclerosis: the BREMS score
  1. Roberto Bergamaschi (roberto.bergamaschi{at}mondino.it)
  1. Neurological Institute C.Mondino, Pavia, Italy
    1. Silvana Quaglini (silvana.quaglini{at}unipv.it)
    1. Department of Informatics and System Sciences, University of Pavia, Italy
      1. Maria Trojano (mtrojano{at}neurol.uniba.it)
      1. University of Bari, Italy
        1. Maria Pia Amato (mariapia.amato{at}unifi.it)
        1. University of Florence, Italy
          1. Eleonora Tavazzi (eleonora.tavazzi{at}mondino.it)
          1. Neurological Institute C.Mondino, Pavia, Italy
            1. Damiano Paolicelli (dpaolicelli{at}neurol.uniba.it)
            1. Department of Neurological and Psychiatric Sciences, Bari, Italy
              1. Valentina Zipoli (valentina.zipoli{at}unifi.it)
              1. Department of Neurological and Psychiatric Sciences, Florence, Italy
                1. Alfredo Romani (alfredo.romani{at}mondino.it)
                1. Neurological Institute C.Mondino, Pavia, Italy
                  1. Aurora Fuiani (afuiani{at}neurol.uniba.it)
                  1. Department of Neurological and Psychiatric Sciences, University of Bari, Italy
                    1. Emilio Portaccio (emilio.portaccio{at}unifi.it)
                    1. Department of Neurological and Psychiatric Sciences, Florence, Italy
                      1. Carlo Berzuini (carlo.berzuini{at}unipv.it)
                      1. Department of Informatics and System Sciences, University of Pavia, Italy
                        1. Cristina Montomoli (cristina.montomoli{at}unipv.it)
                        1. Department of Health Sciences, Section of Medical Statistics and Epidemiology, University of Pavia, Italy
                          1. Stefano Bastianello (stefano.bastianello{at}unipv.it)
                          1. Neurological Institute C.Mondino, Pavia, Italy
                            1. Vittorio Cosi (vittorio.cosi{at}unipv.it)
                            1. Neurological Institute C.Mondino, Pavia, Italy

                              Abstract

                              Aim: We propose a simple tool for early prediction of unfavorable long-term evolution of multiple sclerosis (MS). Methods: A Bayesian model allowed us to calculate, within the first year of disease and for each patient, the Bayesian Risk Estimate for MS (BREMS) score that represents the risk of reaching secondary progression (SP).

                              Results: The median BREMS were higher in 158 patients who reached SP within 10 years in comparison with 1087 progression-free patients (0.69 vs. 0.30, p<0.0001). BREMS value was related to SP-risk in the whole cohort (p<0.0001) and in the subgroup of 535 patients who had never been treated with immune therapies, thus fairly representing the natural history of disease (p<0.000001).

                              Conclusions: BREMS can be useful both to identify the patients who are candidates or not for early or for more aggressive therapies, and to improve the design and the analysis of clinical therapeutic trials and of observational studies.

                              • clinical research methods
                              • disease progression
                              • multiple sclerosis

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